Dear useR's,
I was comparing results for a logistic regression model between different
library's.
themodel formula is arranged as follows:
response ~ (intercept) + value + group
OR:
glm( response ~ (intercept) + value + group ,
family=binomial(link='logit'))
lrm( response ~ (intercept) + value + group )
ROC( from = response ~ (intercept) + value + group , plot='ROC')
the response is a binary vaiable,
the independent predictor 'value' is a continuous variable,
and the grouping factor is a ordered factor (with 5 levels
(25,50,100,200,400))
When I compare the GLM model with the ROC model and the LRM model setting
'group' as factor variable,
the resulting coefficients are similar to eachother.
When I set 'group' as an ordered factor variable (as it should be) the GLM
model with the ROC model coefficients are still comparable,
but the LRM coefficients are completely different.
I have looked up the Design package, and there is a function 'cr.setup',
which sets up an ordinal logistic response, this is however not the case
here.
the model hase a binary response (0 or 1), a continuos predicter and a
ordered grouping factor.
Does anybody know what I am doing wrong ?
Thanks for you time,
Tom
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